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Sample Sizes for Comparing Rating Scales to a Benchmark

Is product satisfaction above average? Is it best in class? Do customers have a favorable or unfavorable opinion of the current product? When UX researchers want to measure attitudes and intentions, they often ask respondents to complete multipoint items like the one shown in Figure 1. It’s also common to set a target benchmark for

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Sample Sizes for Rating Scale Confidence Intervals

Sample size computations can seem like an art. Some assumptions are involved when computing sample sizes, but it should be more math than magic. A key ingredient needed to cook up a sample size estimate is the standard deviation. You need yeast to make bread, and you need a measure of variability to make an

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How to Estimate the Standard Deviation for Rating Scales

The standard deviation is the most common measure of variability. It’s less intuitive than measures of central tendency such as the mean, but it plays an essential role in analysis and sample size planning. The standard deviation is a key ingredient when building a confidence interval and can be easily computed from a sample of

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rating scale data grid with foreground text reading: The Variability and Reliability of Standardized UX Scales

The Variability and Reliability of Standardized UX Scales

In an earlier article, we examined a large dataset of rating scale data. After analyzing over 100,000 individual responses from 4,048 multipoint items across 25 studies, we reported the typical standard deviations for five-, seven-, and eleven-point items. We found that the average standard deviation tended to be around 25% of the maximum range of

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Sample Sizes for a SUS Score

Despite its age and the availability of other UX measures such as the UX-Lite™ and SUPR-Q®, the ten-item System Usability Scale (SUS) is still a very popular measure. It’s used widely in benchmark tests of software products to generate an overall score of perceived usability. We regularly collect SUS scores for dozens of consumer and

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